Estimating above-ground biomass of corn by combining satellite imagery and field inventory

Nguyen Cong Hieu, Jeahoon Jung, Jeonghyun Kim, Shuhe Zhao, Joon Heo

Research output: Contribution to conferencePaper

Abstract

Recently, crop growth monitoring has been become urgently needed through the growing season in order to estimate crop yields. Above-ground biomass is one of important crop parameters that can be monitored and assessed by remote sensing technology. In this paper, we aim to estimate above-ground biomass of corn using a non parametric method, k-nearest Neighbors algorithm (kNN) which to extract information about spatial distribution and total carbon stock. A case study area was selected in Shandong Province of China. The satellite image HJ-1 data and Corn biomass were acquired by the synchronous date. Fifteen band ratios were produced and Pearson correlation between spectral properties and corresponding ground data was tested. Based on the analysis, five optimal band ratios were chosen as independent variables. Also, number of k in kNN algorithm was tested; it is from 5 to 15. As a result, the best RMSE is estimated at 118.213 g/m2 when k is 15. Though the number of samples is limited, a model combining HJ-1 and kNN algorithm could be used to create map of above-ground corn biomass.

Original languageEnglish
Publication statusPublished - 2014 Jan 1
Event35th Asian Conference on Remote Sensing 2014: Sensing for Reintegration of Societies, ACRS 2014 - Nay Pyi Taw, Myanmar
Duration: 2014 Oct 272014 Oct 31

Other

Other35th Asian Conference on Remote Sensing 2014: Sensing for Reintegration of Societies, ACRS 2014
CountryMyanmar
CityNay Pyi Taw
Period14/10/2714/10/31

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications

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    Hieu, N. C., Jung, J., Kim, J., Zhao, S., & Heo, J. (2014). Estimating above-ground biomass of corn by combining satellite imagery and field inventory. Paper presented at 35th Asian Conference on Remote Sensing 2014: Sensing for Reintegration of Societies, ACRS 2014, Nay Pyi Taw, Myanmar.